Artificial Intelligence & Machine Learning
Tuesday, July 11, 2023
Brief History
The hype cycle
The Gartner Hype Cycle.
The hype cycle (circa 2016)
The Gartner Hype Cycle.
Applications in healthcare
The data types considered in the artificial intelligence artificial (AI) literature. The comparison is obtained through searching the diagnosis techniques in the AI literature on the PubMed database(Jiang et al. 2017, fig. 1).
Applications in healthcare
The leading 10 disease types considered in the artificial intelligence (AI) literature. The first vocabularies in the disease names are displayed. The comparison is obtained through searching the disease types in the AI literature on PubMed (Jiang et al. 2017, fig. 3).
Applications in healthcare
from (Challen et al. 2019)
Classes of Machine Learning
Broad classes of machine learning.
A map of machine learning approaches
Supervised Learning
Supervised Learning
Supervised learning.
Supervised Learning
Supervised learning.
Supervised Learning
Supervised learning.
Classification
Regression
Application of regression to predict eutrophil count using the signatures learned from public databases(Oh et al. 2022, fig. 4)
Algorithms for Supervised Learning
Linear regression.
Algorithms for Supervised Learning
Anscombe’s Quartet
Algorithms for Supervised Learning
Classification and Regression Trees (CART).
Algorithms for Supervised Learning
K-nearest neighbor (kNN) algorithm.
Algorithms for Supervised Learning
Random Forests.
Algorithms for Supervised Learning
Deep learning.
Applying Supervised Learning Algorithms
The mlr3 ecosystem in R.
Unsupervised Learning
Unsupervised learning.
Clustering
Gene expression measurements.
Classes of unsupervised learning algorithms
Clustering
Thinking about similarities and differences
Clustering
Algorithm for hierarchical clustering
Clustering
Algorithm for hierarchical clustering
Clustering
Algorithm for hierarchical clustering
Dimensionality Reduction
Schematic PCA.
Dimensionality Reduction
Using dimensionality reduction to explore 22,000 dimensions of gene expression data on 280 samples.
Ethical challenges in AI & ML
How do we address ethical and moral decisionmaking for individuals, groups, and society?
Biases in AI/ML
Types and sources of bias in big data and AI/ML applications in healthcare (Norori et al. 2021). Bias in the medical field can be dissected along three directions: data-driven, algorithmic, and human.
Addressing Bias
Open science practices can assist in moving toward fairness in AI for health care (Norori et al. 2021). These include:
- participant-centered development of AI algorithms and participatory science;
- responsible data sharing and inclusive data standards to support interoperability;
- code sharing, including sharing of AI algorithms that can synthesize underrepresented data to address bias and improve health outcomes.
Each of these is harder and more expensive than it sounds.